# Removing grid structure in angle-resolved photoemission spectra via deep learning method

arXiv: https://arxiv.org/abs/2210.11200 · HTML: https://mlsanity.com/paper/2210.11200/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 20 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- ["Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors](https://mlsanity.com/paper/1812.00467/) (2018)
- [Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization](https://mlsanity.com/paper/2006.08857/) (2020)
- [Deep learning-based statistical noise reduction for multidimensional spectral data](https://mlsanity.com/paper/2107.00844/) (2021)
- [Super Resolution Convolutional Neural Network for Feature Extraction in Spectroscopic Data](https://mlsanity.com/paper/2001.10908/) (2020)
- [Spectroscopic data de-noising via training-set-free deep learning method](https://mlsanity.com/paper/2210.10494/) (2022)

## Built on by (most cited first, 1 of 1)

- [Training-set-free two-stage deep learning for spectroscopic data de-noising](https://mlsanity.com/paper/2402.18830/) (2024)

## Abstract (arXiv)

Spectroscopic data may often contain unwanted extrinsic signals. For example, in ARPES experiment, a wire mesh is typically placed in front of the CCD to block stray photo-electrons, but could cause a grid-like structure in the spectra during quick measurement mode. In the past, this structure was often removed using the mathematical Fourier filtering method by erasing the periodic structure. However, this method may lead to information loss and vacancies in the spectra because the grid structure is not strictly linearly superimposed. Here, we propose a deep learning method to effectively overcome this problem. Our method takes advantage of the self-correlation information within the spectra themselves and can greatly optimize the quality of the spectra while removing the grid structure and noise simultaneously. It has the potential to be extended to all spectroscopic measurements to eliminate other extrinsic signals and enhance the spectral quality based on the self-correlation of the spectra solely.